import os
import pickle
import json
import socket
import json
import face_recognition
import cv2

hostport = ('127.0.0.1',9999)
try:
    s = socket.socket(socket.AF_INET,socket.SOCK_STREAM)  #创建TCP socket
    s.connect(hostport)  #链接套接字
except Exception:
    #连接失败
    print("{'result':false,'msg':'\u8fde\u63a5\u5931\u8d25'}")
    exit()

data_path = "D:/Code/nodejs/socket_face_recognition/faces"
alist = []
allFile = os.walk(data_path)
#获取所有的图片数据
for path, d, filelist in allFile:
	for filename in filelist:
		if filename.endswith('jpg') or filename.endswith('png') or filename.endswith('jpeg'):
			alist.append(os.path.join(path, filename))

# 获取对应的特征
known_face_encodings = []
known_face_names = []

for filename in alist:
	# 获取文件名不包含后缀
	(filepath, tempfilename) = os.path.split(filename)
	(shotname, extension) = os.path.splitext(tempfilename)
	known_face_names.append(shotname)
	#读取文件
	with open(filepath + "/" + shotname + '.dat', 'rb') as file_object:
		known_face_encodings.append(pickle.load(file_object)[0])

video_capture = cv2.VideoCapture(0)

face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_small_frame = small_frame[:, :, ::-1]

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

    
        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"

            # If a match was found in known_face_encodings, just use the first one.
            if True in matches:
                first_match_index = matches.index(True)
                name = known_face_names[first_match_index]
            # s.send(bytes(name,'utf8')) #发送数据到套接字
            print(name)
            face_names.append(name)

    process_this_frame = not process_this_frame


    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # Draw a box around the face
        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

        # Draw a label with a name below the face
        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()